Evaluation of radiological features for breast tumour classification in clinical screening with machine learning methods
| Autor: | Andreas Degenhard, Bert Arnrich, Tim Wilhelm Nattkemper, Linda Pointon, Carmel Hayes, Wiebke Timm, Oliver Lichte, Martin O. Leach |
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| Rok vydání: | 2005 |
| Předmět: |
Computer science
Decision tree Medicine (miscellaneous) clinical screening Breast Neoplasms Machine learning computer.software_genre breast cancer Breast cancer Artificial Intelligence Image Processing Computer-Assisted medicine Humans Cluster analysis computer support Artificial neural network business.industry vector machine (SVM) Decision Trees Pattern recognition medicine.disease Magnetic Resonance Imaging aided diagnosis Radiography Support vector machine machine learning Feature (computer vision) Computer-aided diagnosis Unsupervised learning Female Artificial intelligence business artificial neural networks |
| Zdroj: | Artificial Intelligence in Medicine. 34:129-139 |
| ISSN: | 0933-3657 |
| DOI: | 10.1016/j.artmed.2004.09.001 |
| Popis: | Objective: In this work, methods utilizing supervised and unsupervised machine learning are applied to analyze radiologically derived morphological and calculated kinetic tumour features. The features are extracted from dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) time-course data. Material: The DCE-MRI data of the female breast are obtained within the UK Multicenter Breast Screening Study. The group of patients imaged in this study is selected on the basis of an increased genetic risk for developing breast cancer. Methods: The k-means clustering and self-organizing maps (SOM) are applied to analyze the signal structure in terms of visualization. We employ k-nearest neighbor classifiers (k-nn), support vector machines (SVM) and decision trees (DT) to classify features using a computer aided diagnosis (CAD) approach. Results: Regarding the unsupervised techniques, clustering according to features indicating benign and malignant characteristics is observed to a limited extend. The supervised approaches classified the data with 74% accuracy (DT) and providing an area under the receiver-operator-characteristics (ROC) curve (AUC) of 0.88 (SVM). Conclusion: It was found that contour and wash-out type (WOT) features determined by the radiologists lead to the best SVM classification results. Although a fast signal uptake in early time-point measurements is an important feature for malignant/benign classification of tumours, our results indicate that the wash-out characteristics might be considered as important. © 2004 Elsevier B.V. All rights reserved. |
| Databáze: | OpenAIRE |
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